See the published version of this preprint at Human Genomics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Primary research
James Brody
University of California Irvine
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7995-5197
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Introduction.
The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.
Methods.
We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.
Results.
We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.
Conclusion.
Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
Keywords
COVID-19, genetic risk score, UK Biobank, machine learning
Figure 1
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Human Genomics.
Version 2
Posted 23 Sep, 2020
No community comments so far
Reviewer #2 agreed
On 24 Sep, 2020
Review #2 received
Received 24 Sep, 2020
Editorial decision: Accept
On 24 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Review #1 received
Received 22 Sep, 2020
Editor assigned
On 21 Sep, 2020
Reviewers invited
Invitations sent on 21 Sep, 2020
Submission checks complete
On 20 Sep, 2020
Editor invited
On 20 Sep, 2020
No comments provided
Review #2 received
Received 30 Aug, 2020
Editorial decision: Major revision
On 30 Aug, 2020
Review #1 received
Received 28 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Editor assigned
On 21 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Submission checks complete
On 20 Jul, 2020
Editor invited
On 20 Jul, 2020
First submitted
On 17 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
Learn more about our company.
See the published version of this preprint at Human Genomics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Primary research
James Brody
University of California Irvine
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7995-5197
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Human Genomics.
Version 2
Posted 23 Sep, 2020
No community comments so far
Reviewer #2 agreed
On 24 Sep, 2020
Review #2 received
Received 24 Sep, 2020
Editorial decision: Accept
On 24 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Review #1 received
Received 22 Sep, 2020
Editor assigned
On 21 Sep, 2020
Reviewers invited
Invitations sent on 21 Sep, 2020
Submission checks complete
On 20 Sep, 2020
Editor invited
On 20 Sep, 2020
No comments provided
Review #2 received
Received 30 Aug, 2020
Editorial decision: Major revision
On 30 Aug, 2020
Review #1 received
Received 28 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Editor assigned
On 21 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Submission checks complete
On 20 Jul, 2020
Editor invited
On 20 Jul, 2020
First submitted
On 17 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Human Genomics.
Introduction.
The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.
Methods.
We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.
Results.
We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.
Conclusion.
Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
Figure 1
Loading...